(OOC-ML) It's long be aware that the sequence order of training data exposed to the model has an impact on model generalizability. Active learning [1]} emphasises to actively query data in a iterative fashion to only expose the model to data close to he decision boundary . Curriculum learning [2]} emphasises to sort all training so that the model is exposed to challenges of increasing difficulty. This element of data logistics has also been closely weaved into many optimization algorithms that aim to improve model generalizability. For example, contrastive loss[3]} requires certain ratio of positive v.s. negative samples in each mini-bath. Most related to portal learning is meta-learning which can be categorized into metric-based, model-based and optimizer based “learn to learn” algorithms [4]} with application to few-shot learning and zero-shot learning. Meta-learning started for the data-efficiency challenge instead of generalization or OOD. Although meta-learning is defined very general, making many algorithms seem to be mere an variants falling under its umbrella, in practice, algorithms proposed bearing the name of meta-learning are defined on multi-class classification data set, typically image classification, where the main challenge is the huge number of classes while limited data points are known in each class. Because of this underlying motivation, meta-learning features a very involving data logistics with multiple layer of optimization each has its meta-train/meta-test set sampled based on label distribution. These unspecified facts reveal that there is no existing meta-learning algorithm fit into CPI data.
